AI is driving breakthroughs in space travel and could lead to nuclear-powered rockets, UND scholars write in The Conversation
Editor’s note: November 24th, conversation Published paper co-authored by UND scholars Marcos Fernandez-Tooth Assistant Professor of Space Research. Preity Nile Master’s student in Aerospace Science. Sai Susmita Gudanti, PhD candidate in aerospace science. and This article outlines how artificial intelligence is improving the efficiency of rocket propulsion and accelerating development in this field.
The article can be found below and read in its original format The Conversation website. As of Dec. 4, the article has been republished by 10 media outlets and read more than 7,000 times, including by readers in the United States, Canada, Japan, Australia, the United Kingdom, and more.
The Conversation is a non-profit organization A media resource that publishes “explanatory journalism” articles by university academics and allows those articles to be republished instantly and free of charge.n. A complete list of all papers written by UND scholars Available on The Conversation website.
A must-read for UND faculty and graduate students interested in learning more about writing for The Conversation. introduction to conversation, Story published in UND Today in 2022. Additional stories in 2023, “Over 340,000 readers worldwide.” We noted that UND faculty who contributed to The Conversation reported an “outstanding” experience and said they would have no hesitation in recommending their colleagues to become Conversation authors.
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Written by Marcos Fernandes Tooth, Preeti Nar, Sai Susumisha Gudanti, Sreejith Vidyadharan Nar
Every year, companies and space agencies launch hundreds of rockets into space – and that number is set to increase dramatically with ambitious missions to the Moon, Mars, and beyond. But these dreams hinge on one important challenge: promotion. Propulsion is the method used to propel a rocket or spacecraft forward.
To make interplanetary travel faster, safer and more efficient, scientists need breakthrough advances in propulsion technology. artificial intelligence is one type of technology that is beginning to provide some of these needed breakthroughs.
we are the next team engineers and graduate students These are people who study AI in general and how the subset of AI that we call AI works. machine learning In particular, it can change the propulsion of the spacecraft. From optimization nuclear heat engine Up to complex management Plasma confinement in fusion systemsAI is reshaping the design and operation of propulsion. It is rapidly becoming an indispensable partner in humanity’s journey to the stars.
Machine learning and reinforcement learning
Machine learning is a branch of AI that identifies patterns in data that have not been explicitly trained. It’s a vast field have their own branchthere are many applications. Each branch emulates intelligence in different ways, including recognizing patterns, parsing and generating language, and learning from experience. This last subset in particular is commonly known as: reinforcement learningteaches machines how to perform tasks by evaluating their performance, allowing them to continually improve through experience.
As a simple example, imagine a chess player. Rather than calculating every move, players recognize patterns by playing thousands of matches. Reinforcement learning creates similar intuitive expertise in machines and systems, but at a computational speed and scale that is impossible for humans. learn through experience and repetition By observing the environment. These observations allow the machine to correctly interpret each result and allow the system to deploy the optimal strategy to achieve the goal.
Reinforcement learning can improve human understanding of highly complex systems, challenging the limits of human intuition. It helps you decide what’s most important. Efficient trajectory of spacecraft It can head anywhere in space and does so by optimizing the thrust needed to send the spacecraft there. Also, potentially Design better propulsion systemsfrom choosing the best materials to devising configurations that more efficiently transfer heat between parts within the engine.
Reinforcement learning for propulsion systems
When it comes to space propulsion, reinforcement learning generally falls into two categories. One to assist in the design phase (when engineers define mission needs and system capabilities) and one in support. real-time operation Once the spaceship takes flight.
One of the most exotic and promising propulsion concepts is nuclear propulsion. It harnesses the same force that powers the atomic bomb and fuels the sun. nuclear fission and fusion.
Nuclear fission occurs by splitting heavy atoms Releasing energy such as uranium or plutonium is the principle used in most ground-based nuclear reactors. On the other hand, fusion combine light atoms Things like hydrogen produce even more energy, but require more extreme conditions to start it.
Nuclear fission is a more mature technology that has been tested in several space propulsion prototypes. This is how it is used in space as well. radioactive isotope thermoelectric generatorlike them provided power to the Voyager spacecraft. But fusion remains an attractive frontier.
nuclear thermal propulsion One day, it may be possible to transport spacecraft to Mars and beyond at a lower cost than simply burning fuel. it will get the craft there faster electric propulsionuses a heated gas consisting of charged particles called plasma.
Unlike these systems, nuclear propulsion relies on the heat generated from atomic reactions. That heat is transferred to the propellant, usually hydrogen, which expands and exits the nozzle to create thrust and launch the aircraft forward.
So how can reinforcement learning help engineers develop and operate these powerful technologies? Let’s start with design.
The role of reinforcement learning in design
Early nuclear thermal propulsion designs from the 1960s, such as those from NASA Nerva planused solid uranium fuel formed into prismatic blocks. Since then, engineers have been using ceramic pebble beds to Grooved ring with intricate channels.
Why were so many experiments conducted? Because the more efficiently a reactor can transfer heat from fuel to hydrogen, the more thrust it produces.
Reinforcement learning has proven essential in this field. Optimizing the geometry and heat flow between fuel and propellant is a complex problem involving countless variables, from material properties to the amount of hydrogen flowing through the reactor at any given moment. Reinforcement learning can analyze these design variations and identify configurations that maximize heat transfer. Imagine this as a smart thermostat for a rocket engine. Given the extreme temperatures involved, you definitely don’t want to go near it.
Reinforcement learning and fusion technology
Reinforcement learning also plays an important role in the development of fusion technology. large-scale experiments such as JT-60SA Tokamak Japan is pushing the limits of fusion energy, but its massive size makes it impractical for spaceflight. That’s why researchers are conducting research Compact design such as polywell. These exotic devices look like hollow cubes just a few inches in diameter and trap plasma in a magnetic field, creating the conditions necessary for nuclear fusion.
Control of magnetic field Working inside a polywell is not an easy task. The magnetic field must be strong enough to keep the hydrogen atoms bouncing around until they fuse. This process requires a tremendous amount of energy to start, but once started, it can be self-sustaining. Extending this technology to nuclear thermal propulsion requires overcoming this challenge.
Reinforcement learning and energy generation
However, the role of reinforcement learning goes beyond design. This helps manage fuel consumption, a critical task for missions that need to adapt on the fly. Today’s space industry is increasingly interested in spacecraft that can play a variety of roles, depending on how they adapt to mission needs and changing priorities over time.
For example, military applications require rapid response to changing geopolitical scenarios. Examples of technologies that have adapted to rapid change include: Lockheed Martin LM400 Satellites with various functions such as missile warning and remote sensing.
However, this flexibility introduces uncertainty. How much fuel does a mission require? And when will it be needed? Reinforcement learning helps with these calculations.
From bicycles to rockets, whether human or machine, learning through experience will shape the future of space exploration. AI will play an increasing role in space travel as scientists push the boundaries of propulsion and intelligence. It could help scientists explore the solar system and beyond, opening the door to new discoveries.
